Continuous control with deep reinforcement learning

We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain. We present an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. Using the same learning algorithm, network architecture and hyper-parameters, our algorithm robustly solves more than 20 simulated physics tasks, including classic problems such as cartpole swing-up, dexterous manipulation, legged locomotion and car driving. Our algorithm is able to find policies whose performance is competitive with those found by a planning algorithm with full access to the dynamics of the domain and its derivatives. We further demonstrate that for many of the tasks the algorithm can learn policies end-to-end: directly from raw pixel inputs.

This project is an exercise in reinforcement learning as part of the Machine Learning Engineer Nanodegree from Udacity. The idea behind this project is to teach a simulated quadcopter how to perform some activities.

This tool is developed to scrape twitter data, process the data, and then create either an unsupervised network to identify interesting patterns or can be designed to specifically verify a concept or idea.